Currently, in histopathological diagnosis performed as definitive diagnosis of cancers, clinical pathologists who have specialized knowledge and experience (hereinafter referred to simply as “pathologists”) discriminate between tissues of normal and disease based on microscopic observation of histopathological specimens taken from patients through surgery or examination. Herein, the term “cancer” refers to general malignant neoplasm, and this term is discriminated from the term “malignant neoplasm arising from epithelial tissue” which is used as “gastric biopsy tissue”.
In recent years, however, heavy burdens on the pathologists have become a serious social issue due to the increasing number of cancer patients and the severe shortage of pathologists. Based on the data published by the Center for Cancer Control and Information Services of the National Cancer Center, the number of patients newly diagnosed as having cancer every year is more than 500,000since 1998in Japan. In 2004,the newly-diagnosed cancer patients amounted to approximately 650,000 which is about three times the number for 1975. The number of the cancer patients is expected to be furthermore increasing, and expected additional burdens imposed on the pathologists are of public concern. However, there are no cures for the pathologist shortage, and there is an urgent need for development of medical technologies to assist in reducing the burdens imposed on the pathologists.
As one of the solutions to the above-mentioned problem, a technique for automatic cancer diagnosis was proposed to extract features of the nuclei and cells from a histopathological image (refer to non-patent documents 1and 2). Information obtainable from the techniques disclosed in the non-patent documents 1and 2is significantly affected by the accuracy of clipping the nuclei.
Then, the inventors of the present invention proposed a pathological diagnosis support technique using higher-order local auto-correlation (HLAC) as another approach to solving the problem. For the proposed pathological diagnosis support technique, refer to non-patent documents 4and 5. For the HLAC, refer to non-patent document 3. The proposed technique allows feature extraction of a histopathological image without clipping the nucleus and cell contours. In an experiment according to the proposed technique, first, HLAC features were extracted from a histopathological image of a gastric biopsy and HLAC features were extracted from a normal histopathological image of a normal tissue. The HLAC features extracted from the normal histopathological image were subjected to principal component analysis to form a normal subspace (refer to non-patent document 6). Anomalies were detected by calculating a degree of deviation of the HLAC features extracted from the gastric biopsy histopathological image from the normal subspace. As a result of the experiment, histopathological images including malignant neoplasm arising from epithelial tissue were recognized as anomaly, as compared with the normal histopathological images which had been learned and were known to be non-cancerous. Thus, it was confirmed that the proposed technique was applicable to automatic diagnosis.
As disclosed in JP2006-153742A (patent document 1), typical color information on nuclei derived from many histopathological images is stored in advance. The color distribution is examined in a target histopathological image. A highly distributed portion is regarded as the center of a nucleus and clipped in a predetermined size. Then, the color information on the clipped portion is employed as the features of the target histopathological image.
As disclosed in JP2009-9290A (patent document 2), a histopathological image is converted into an HSV color space to obtain a saturation (S) component and a brightness (V) component. The saturation and brightness components are binarized by binarization method based on discriminant analysis, and then are logically multiplied. A region having a value of zero is regarded as a background. Further, the brightness (V) component is binarized by the binarization method based on discriminant analysis to extract a nucleus from the regions other than the background. In this conventional technique, a histogram of area ratio of a cytoplasm and a nucleus per cell is employed as a feature.